Advanced solid-state welding based on computational manufacturing using the additive manufacturing process

被引:3
|
作者
Shah, Preet Ashok [1 ]
Srinath, M. K. [1 ]
Gayathri, R. [1 ]
Puvandran, P. [1 ]
Selvaraj, Senthil Kumaran [2 ]
机构
[1] Vellore Inst Technol VIT, Sch Mech Engn SMEC, Vellore 632014, Tamil Nadu, India
[2] Vellore Inst Technol VIT, Sch Mech Engn SMEC, Dept Mfg Engn, Vellore 632014, Tamil Nadu, India
来源
INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM | 2023年
关键词
IoT; Solid-state welding; Robotics; AI; Machine learning; Additive manufacturing; Digital manufacturing; Hybrid additive manufacturing; ARTIFICIAL NEURAL-NETWORKS; STRUCTURAL INTEGRITY; GENETIC ALGORITHM; STRENGTH; INTERNET; SYSTEM; JOINTS; MICROSTRUCTURE; OPTIMIZATION; DESIGN;
D O I
10.1007/s12008-023-01243-6
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The world is undergoing a paradigm shift in how products are manufactured. The fourth industrial revolution, or Industry 4.0, is given greater importance by industries and companies worldwide as they bridge the gap between technology and manufacturing processes. Digital manufacturing has become a key research area as it can boost the productivity of all manufacturing processes by using advanced techniques such as machine learning, the Internet of Things, and artificial intelligence. These techniques have been used in tandem with numerous hybrid additive manufacturing processes to increase precision, create unlimited complex geometries, reduce material waste, change the constraints of the part during manufacturing if needed, and reduce the capital requirement. However, conventional welding techniques were harmful to humans, provided a poor surface finish, and proved difficult for welding complex parts. With hybrid additive manufacturing techniques combined with conventional welding techniques, conventional welding processes have been automated, reducing human intervention. This can be done using some of the latest technologies and concepts invented, such as robots, cobots, artificial intelligence, and virtual reality. Some of these new concepts and technologies, such as machine learning, virtual reality, and the Internet of Things, can optimize various welding parameters and improve remote welding technology. In addition, this paper thoroughly explores four important IT-based domains of digital manufacturing used in welding processes: robots, the Internet of Things, machine learning, and artificial intelligence. These concepts have influenced traditional welding processes by presenting the various parameters used to examine the impact. In addition, this paper also considers the potential scope of research. Also, this paper would look at different digital manufacturing processes and how they are used to make hybrid parts based on additive manufacturing.
引用
收藏
页数:27
相关论文
共 50 条
  • [41] A novel friction and rolling based solid-state additive manufacturing method: Microstructure and mechanical properties evaluation
    Xie, Ruishan
    Shi, Yanchao
    Liu, Haibin
    Chen, Shujun
    MATERIALS TODAY COMMUNICATIONS, 2021, 29 (29):
  • [42] Review of Particle-Based Computational Methods and Their Application in the Computational Modelling of Welding, Casting and Additive Manufacturing
    Tong, Mingming
    METALS, 2023, 13 (08)
  • [43] Additive Friction Stir-Enabled Solid-State Additive Manufacturing for the Repair of 7075 Aluminum Alloy
    Griffiths, R. Joey
    Petersen, Dylan T.
    Garcia, David
    Yu, Hang Z.
    APPLIED SCIENCES-BASEL, 2019, 9 (17):
  • [44] Ductile and high strength Cu fabricated by solid-state cold spray additive manufacturing
    Chen, Chaoyue
    Xie, Yingchun
    Yin, Shuo
    Li, Wenya
    Luo, Xiaotao
    Xie, Xinliang
    Zhao, Ruixin
    Deng, Chunming
    Wang, Jiang
    Liao, Hanlin
    Liu, Min
    Ren, Zhongming
    JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY, 2023, 134 : 234 - 243
  • [45] Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning
    Yi Han
    R. Joey Griffiths
    Hang Z. Yu
    Yunhui Zhu
    Journal of Materials Research, 2020, 35 : 1936 - 1948
  • [46] Interfacial corrosion behavior of aluminum/steel joints prepared by solid-state additive manufacturing
    Li, Yidi
    Teng, Jianwei
    Wang, Jun
    Wang, Hui
    Liu, Qinglin
    Lai, Ruilin
    Yang, Biaobiao
    Wang, Zhongchang
    Li, Yunping
    CORROSION SCIENCE, 2025, 244
  • [47] Key role of temperature on delamination in solid-state additive manufacturing via supersonic impact
    Wang, Qian
    Ma, Ninshu
    Huang, Wenjia
    Shi, Junmiao
    Luo, Xiao-Tao
    Tomitaka, Sora
    Morooka, Satoshi
    Watanabe, Makoto
    MATERIALS RESEARCH LETTERS, 2023, 11 (09): : 742 - 748
  • [48] Quantitative microstructure analysis for solid-state metal additive manufacturing via deep learning
    Han, Yi
    Griffiths, R. Joey
    Yu, Hang Z.
    Zhu, Yunhui
    JOURNAL OF MATERIALS RESEARCH, 2020, 35 (15) : 1936 - 1948
  • [49] Solid-state production of uniform metal powders for additive manufacturing by ultrasonic vibration machining
    Wang, Yaoke
    Landis, Malachi
    Ekaputra, Clement
    Vita, Valeria
    Guo, Ping
    ADDITIVE MANUFACTURING, 2024, 81
  • [50] Friction welding: An effective joining process for hybrid additive manufacturing
    Dwivedi, Mrinal
    Silvestri, Alessia Teresa
    Franchitti, Stefania
    Krishnaswamy, Hariharan
    Narayanaperumal, Arunachalam
    Astarita, Antonello
    CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2021, 35 : 460 - 473